Computational and Data Systems Initiative
The goal of this workshop is to serve as an introduction to the theory and implementation of hierarchical bayesian models. Specifically, we will cover theoretical and practical considerations for fitting random effect/nested random effect models and generating meaningful predictions and inferences. Further, we will discuss hierarchical models for longitudinal and time series data. Participants will get access to several worked examples written in STAN and an example in INLA.
At the end of this workshop, you will:
> Understand the difference between Bayesian random effect and fixed effect models;
> Fitting random effect and nested random effect models in Stan;
> Priors for hierarchical models and model complexity;
> Introduction to hierarchical Bayesian models for longitudinal and time series models;
> Understanding a hierarchical longitudinal model fit in INLA (if time permits).
*Note the time of this workshop differs from our usual statistics workshops.
Date: Monday April 11th, 2022.
Time: 11am to 1pm.
Location: online via Zoom.
Instructor: Tyrel Stokes, PhD student, Dept. of Mathematics and Statistics.